scholarly journals Evaluation of two workflows for whole genome sequencing-based typing of influenza A viruses

2019 ◽  
Vol 266 ◽  
pp. 30-33 ◽  
Author(s):  
Daniel Wüthrich ◽  
Daniela Lang ◽  
Nicola F. Müller ◽  
Richard A. Neher ◽  
Tanja Stadler ◽  
...  
2019 ◽  
Author(s):  
Marina Escalera-Zamudio ◽  
Ana Georgina Cobián-Güemes ◽  
Blanca Taboada ◽  
Irma López-Martínez ◽  
Joel Armando Vázquez-Pérez ◽  
...  

ABSTRACTThe constant threat of emergence for novel pathogenic influenza A viruses with pandemic potential, makes full-genome characterization of circulating influenza viral strains a high priority, allowing detection of novel and re-assorting variants. Sequencing the full-length genome of influenza A virus traditionally required multiple amplification rounds, followed by the subsequent sequencing of individual PCR products. The introduction of high-throughput sequencing technologies has made whole genome sequencing easier and faster. We present a simple protocol to obtain whole genome sequences of hypothetically any influenza A virus, even with low quantities of starting genetic material. The complete genomes of influenza A viruses of different subtypes and from distinct sources (clinical samples of pdmH1N1, tissue culture-adapted H3N2 viruses, or avian influenza viruses from cloacal swabs) were amplified with a single multisegment reverse transcription-PCR reaction and sequenced using Illumina sequencing platform. Samples with low quantity of genetic material after initial PCR amplification were re-amplified by an additional PCR using random primers. Whole genome sequencing was successful for 66% of the samples, whilst the most relevant genome segments for epidemiological surveillance (corresponding to the hemagglutinin and neuraminidase) were sequenced with at least 93% coverage (and a minimum 10x) for 98% of the samples. Low coverage for some samples is likely due to an initial low viral RNA concentration in the original sample. The proposed methodology is especially suitable for sequencing a large number of samples, when genetic data is urgently required for strains characterization, and may also be useful for variant analysis.


Author(s):  
Beatriz Mengual-Chuliá ◽  
Andrés Alonso-Cordero ◽  
Laura Cano ◽  
M. del Mar Mosquera ◽  
Patricia de Molina ◽  
...  

Molecular surveillance by whole genome sequencing was used to monitor the susceptibility of circulating Influenza A viruses to three polymerase complex inhibitors. A total of 12 resistance substitutions were found among 285 genomes analysed, but none associated with high levels of resistance. Natural resistance to these influenza A antivirals is currently uncommon.


2020 ◽  
pp. 104063872093387
Author(s):  
Patrick K. Mitchell ◽  
Brittany D. Cronk ◽  
Ian E. H. Voorhees ◽  
Derek Rothenheber ◽  
Renee R. Anderson ◽  
...  

Epidemics of H3N8 and H3N2 influenza A viruses (IAVs) in dogs, along with recognition of spillover infections from IAV strains typically found in humans or other animals, have emphasized the importance of efficient laboratory testing. Given the lack of active IAV surveillance or immunization requirements for dogs, cats, or horses imported into the United States, serotype prediction and whole-genome sequencing of positive specimens detected at veterinary diagnostic laboratories are also needed. The conserved sequences at the ends of the viral genome segments facilitate universal amplification of all segments of viral genomes directly from respiratory specimens. Although several methods for genomic analysis have been reported, no optimization focusing on companion animal strains has been described, to our knowledge. We compared 2 sets of published universal amplification primers using 26 IAV-positive specimens from dogs, horses, and a cat. Libraries prepared from the resulting amplicons were sequenced using Illumina chemistry, and reference-based assemblies were generated from the data produced by both methods. Although both methods produced high-quality data, coverage profiles and base calling differed between the 2 methods. The sequence data were also used to identify the subtype of the IAV strains sequenced and then compared to standard PCR assays for neuraminidase types N2 and N8.


2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Hong Kai Lee ◽  
Chun Kiat Lee ◽  
Julian Wei-Tze Tang ◽  
Tze Ping Loh ◽  
Evelyn Siew-Chuan Koay

2017 ◽  
Author(s):  
Emily J. Goldstein ◽  
William T. Harvey ◽  
Gavin S. Wilkie ◽  
Samantha J. Shepherd ◽  
Alasdair R. MacLean ◽  
...  

AbstractGenetic surveillance of seasonal influenza is largely focused upon sequencing of the haemagglutinin gene. Consequently, our understanding of the contribution of the remaining seven gene segments to the evolution and epidemiological dynamics of seasonal influenza is relatively limited. The increased availability of next generation sequencing technologies allows rapid and economic whole genome sequencing (WGS). Here, 150 influenza A(H3N2) positive clinical specimens with linked epidemiological data, from the 2014/15 season in Scotland, were sequenced directly using both Sanger sequencing of the HA1 region and WGS using the Illumina MiSeq platform. Sequences generated by both methods were highly consistent and WGS provided on average >90% whole genome coverage. As reported in other European countries during 2014/15, all strains belonged to genetic group 3C, with subgroup 3C.2a predominating. Inter-subgroup reassortants were identified (9%), including three 3C.3 viruses descended from a single reassortment event, which had persisted in the population. Significant phylogenetic associations with cases of severe acute respiratory illness observed herein warrant further investigation. Severe cases were also more likely to be associated with reassortant viruses (odds ratio: 4.4 (1.3-15.5)) and occur later in the season. These results suggest that increased levels of WGS, linked to clinical and epidemiological data, could improve influenza surveillance.


PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234869
Author(s):  
Xiao-Nan Zhao ◽  
Han-Ju Zhang ◽  
Duo Li ◽  
Jie-Nan Zhou ◽  
Yao-Yao Chen ◽  
...  

2021 ◽  
Author(s):  
Klaudia Chrzastek ◽  
Chandana Tennakoon ◽  
Dagmara Bialy ◽  
Graham L Freimanis ◽  
John Flannery ◽  
...  

Background: Non-targeted whole genome sequencing is a powerful tool to comprehensively identify constituents of microbial communities in a sample. There is no need to direct the analysis to any identification before sequencing which can decrease the introduction of bias and false negatives results. It also allows the assessment of genetic aberrations in the genome (e.g., single nucleotide variants, deletions, insertions and copy number variants) including in noncoding protein regions. Methods: The performance of four different random priming amplification methods to recover RNA viral genetic material of SARS-CoV-2 were compared in this study. In method 1 (H-P) the reverse transcriptase (RT) step was performed with random hexamers whereas in methods 2-4 RT incorporating an octamer primer with a known tag. In methods 1 and 2 (K-P) sequencing was applied on material derived from the RT-PCR step, whereas in methods 3 (SISPA) and 4 (S-P) an additional amplification was incorporated before sequencing. Results: The SISPA method was the most effective and efficient method for non-targeted/random priming whole genome sequencing of COVID that we tested. The SISPA method described in this study allowed for whole genome assembly of SARS-CoV-2 and influenza A(H1N1)pdm09 in mixed samples. We determined the limit of detection and characterization of SARS-CoV-2 virus which was 103 pfu/ml (Ct, 22.4) for whole genome assembly and 101 pfu/ml (Ct, 30) for metagenomics detection. Conclusions: The SISPA method is predominantly useful for obtaining genome sequences from RNA viruses or investigating complex clinical samples as no prior sequence information is needed. It might be applied to monitor genomic virus changes, virus evolution and can be used for fast metagenomics detection or to assess the general picture of different pathogens within the sample.


2018 ◽  
Vol 9 ◽  
Author(s):  
Kazuo Imai ◽  
Kaku Tamura ◽  
Tomomi Tanigaki ◽  
Mari Takizawa ◽  
Eiko Nakayama ◽  
...  

2019 ◽  
Vol 69 (10) ◽  
pp. 1649-1656 ◽  
Author(s):  
Sunando Roy ◽  
John Hartley ◽  
Helen Dunn ◽  
Rachel Williams ◽  
Charlotte A Williams ◽  
...  

Abstract Background Influenza A virus causes annual epidemics in humans and is associated with significant morbidity and mortality. Haemagglutinin (HA) and neuraminidase (NA) gene sequencing have traditionally been used to identify the virus genotype, although their utility in detecting outbreak clusters is still unclear. The objective of this study was to determine the utility, if any, of whole-genome sequencing over HA/NA sequencing for infection prevention and control (IPC) in hospitals. Methods We obtained all clinical samples from influenza (H1N1)-positive patients at the Great Ormond Street Hospital between January and March 2016. Samples were sequenced using targeted enrichment on an Illumina MiSeq sequencer. Maximum likelihood trees were computed for both whole genomes and concatenated HA/NA sequences. Epidemiological data was taken from routine IPC team activity during the period. Results Complete genomes were obtained for 65/80 samples from 38 patients. Conventional IPC analysis recognized 1 outbreak, involving 3 children, and identified another potential cluster in the haemato-oncology ward. Whole-genome and HA/NA phylogeny both accurately identified the previously known outbreak cluster. However, HA/NA sequencing additionally identified unrelated strains as part of this outbreak cluster. A whole-genome analysis identified a further cluster of 2 infections that had been previously missed and refuted suspicions of transmission in the haemato-oncology wards. Conclusions Whole-genome sequencing is better at identifying outbreak clusters in a hospital setting than HA/NA sequencing. Whole-genome sequencing could provide a faster and more reliable method for outbreak monitoring and supplement routine IPC team work to allow the prevention of transmission.


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